Top 20 MLOps Case Studies & Success Stories in 2024
Organizations have started to adopt MLOps practices to standardize and streamline their ML development and operationalization processes. But the journey is not easy and there is much to learn.
We’ve compiled 20 MLOps success stories and case studies to help businesses that are looking to improve their ML processes.
Introducing MLOps to your business
To implement MLOps practices in your business, you need to have a supporting infrastructure. You can either build this infrastructure with your internal resources, or buy an MLOps solution that provides the necessary infrastructure. We will cover both approaches below.
In-house MLOps infrastructure
As we discussed in our previous article, building AI capabilities with internal resources can demand extensive time, effort, and budget. We suggested that most small, and non-tech, companies should work with AI vendors instead of building in-house solutions.
This also applies to MLOps infrastructure. Building a functioning and scalable infrastructure can take over a year and requires hiring additional data scientists, ML engineers, DevOps professionals, etc.
Large companies, like Uber or Facebook, have the resources and the data to afford such investments. However, most companies do not have these resources. More importantly, most of them don’t need such investments because there are capable AI and ML solutions that can easily meet their needs.
Buying an MLOps solution
The other option is buying MLOps solutions that provide the necessary infrastructure to implement MLOps practices in your business. There are tools that cover a subset of MLOps tasks such as:
- Data management
- Modeling
- Operationalization
These tools can be integrated with other solutions which can help you to create an ML pipeline. There are also MLOps platforms that provide end-to-end machine learning lifecycle management. You can explore both types of tools in our in-depth article on MLOps tools.
Aside from the customization opportunities that come with building an in-house MLOps solution, these off-the-shelf MLOps tools can meet the needs of most businesses with rapid deployment at a fraction of the cost.
MLOps case studies
Below is a list of MLOps examples and case studies that we’ve compiled from different vendors and resources:
Customer | Vendor | Country | Industry | Results |
---|---|---|---|---|
AgroScout | ClearML | United States | Agriculture | -Increased data volume 100x without growing the data team -Increased
experiment volume 50x -Decreased the time to production by 50%
|
Booking.com | * | Netherlands | E-Commerce | -Ability to scale AI with 150 customer facing ML models |
CollectiveCrunch | Valohai | Finland | IT | -Reduced the model development time by a factor of five |
Constru | ClearML | Israel | IT | -Reduce the time for reproducing experiments by 50% -Twice as
much ML work handled without additional staff -Projected savings of $1.3 million over the next year
|
Ecolab | Iguazio | United States | Chemicals | Decreased model deployment times from 12 months to 30-90 days |
KONUX | Valohai | Germany | IT | -Running 10X the number of experiments with the same amount
of effort by automated machine orchestration and experiment tracking
|
Levity | Valohai | Germany | IT | -Time and resource savings after failed in-house MLOps projects |
NetApp | Iguazio | United States | IT | -Improved the time to develop and deploy new AI services
by 6-12x -Reduced operating costs by 50%
|
Neural Guard | ClearML | United Kingdom | Aviation | -Saving on cost and shortening time-to-market -Ongoing saving related to
not hiring additional staff
|
NTUC Income | DataRobot | Singapore | Insurance | -Reduced the time to generate results from a few days
to less than an hour
|
Oyak Cement | DataRobot | Turkey | Manufacturing | -Increased alternative fuel usage by 7 times -Cut 2% of
total CO2 emissions -Reduced costs by $39 million
|
Payoneer | Iguazio | United States | Financial Services | -Built a scalable and reliable fraud prediction and prevention model
that analyzes fresh data in real-time and adapts to new threats
|
Philips | ClearML | Netherlands | Healthcare | -Hours saved through streamlined experiment tracking and automatic documentation |
Quadient | Iguazio | France | IT | -Simplified ML development workflow to create AI applications at scale
an, in real time
|
Sharper Shape | Valohai | United States | IT | -Automation of infrastructure and experiment management tasks that takes a
third of data scientists time -New data scientists can be onboarded in a quarter of the time
|
Steward Health Care | DataRobot | United States | Healthcare | -$2 million/year in savings from nurses hours paid per patient
day -$10 million/year savings from reducing patient length of stay
|
The Adecco Group | DataRobot | Switzerland | HR | -37% reduction in the number of CVs reviewed 10% productivity
gain -Launched 60 projects with 3000 models
|
Theator | ClearML | United States | Healthcare | -$130K-$170K annual savings directly related to MLOps |
Trigo | ClearML | Israel | IT | -Streamlined ML workflow with simple experiment tracking, feature store, and
documentation
|
Uber | * | United States | Transportation | -Developed their own ML platform Michelangelo -From zero to hundreds
of ML products in three years thanks to MLOps practices
|
*Companies that build their own MLOps infrastructure
If you need a tool to implement MLOps practices in your business, don’t forget to check our sortable/filterable list of MLOps platforms.
And if you found yourself having more questions, feel free to ask:
Cem has been the principal analyst at AIMultiple since 2017. AIMultiple informs hundreds of thousands of businesses (as per similarWeb) including 60% of Fortune 500 every month.
Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE, NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and media that referenced AIMultiple.
Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised businesses on their enterprise software, automation, cloud, AI / ML and other technology related decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.
He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.
Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.
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